High-Precision Positioning Method for Robot Acoustic Ranging Based on Self-Optimization of Base Stations Article Swipe
Zekai Zhang
,
Jiayu Chen
,
Bishu Gao
,
Yefeng Sun
,
Xiaofeng Ling
,
Z. Li
,
Liang Gong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15105478
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15105478
In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15105478
- OA Status
- gold
- References
- 14
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4410374791Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15105478Digital Object Identifier
- Title
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High-Precision Positioning Method for Robot Acoustic Ranging Based on Self-Optimization of Base StationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-13Full publication date if available
- Authors
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Zekai Zhang, Jiayu Chen, Bishu Gao, Yefeng Sun, Xiaofeng Ling, Z. Li, Liang GongList of authors in order
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https://doi.org/10.3390/app15105478Publisher landing page
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/app15105478Direct OA link when available
- Concepts
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Ranging, Base (topology), Computer science, Acoustics, Telecommunications, Mathematics, Physics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.or | 10 |
| abstract_inverted_index.to | 2, 42, 59, 168 |
| abstract_inverted_index.The | 99 |
| abstract_inverted_index.and | 45, 54, 81, 90, 113, 139, 144 |
| abstract_inverted_index.can | 157 |
| abstract_inverted_index.for | 5, 78, 86, 108, 142 |
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| abstract_inverted_index.the | 3, 13, 37, 46, 109, 126, 154, 160 |
| abstract_inverted_index.This | 65 |
| abstract_inverted_index.base | 87, 96, 134 |
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| abstract_inverted_index.short | 53 |
| abstract_inverted_index.sound | 40 |
| abstract_inverted_index.their | 123 |
| abstract_inverted_index.these | 68 |
| abstract_inverted_index.which | 57 |
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| abstract_inverted_index.modes, | 138 |
| abstract_inverted_index.nearby | 133 |
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| abstract_inverted_index.within | 8, 93 |
| abstract_inverted_index.adopted | 22 |
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| abstract_inverted_index.central | 112 |
| abstract_inverted_index.employs | 140 |
| abstract_inverted_index.enhance | 159 |
| abstract_inverted_index.factors | 44 |
| abstract_inverted_index.further | 82 |
| abstract_inverted_index.methods | 18 |
| abstract_inverted_index.moment, | 128 |
| abstract_inverted_index.ranging | 17, 30, 80 |
| abstract_inverted_index.selects | 129 |
| abstract_inverted_index.signals | 50 |
| abstract_inverted_index.station | 88, 97, 171 |
| abstract_inverted_index.systems | 32, 166 |
| abstract_inverted_index.weights | 141 |
| abstract_inverted_index.accuracy | 162 |
| abstract_inverted_index.acoustic | 16, 29, 49, 79, 164 |
| abstract_inverted_index.boundary | 114 |
| abstract_inverted_index.compared | 167 |
| abstract_inverted_index.confined | 9 |
| abstract_inverted_index.criteria | 107 |
| abstract_inverted_index.distance | 130 |
| abstract_inverted_index.division | 110 |
| abstract_inverted_index.interval | 75 |
| abstract_inverted_index.numerous | 24 |
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| abstract_inverted_index.proposed | 155 |
| abstract_inverted_index.response | 1 |
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| abstract_inverted_index.terminal | 119 |
| abstract_inverted_index.velocity | 41 |
| abstract_inverted_index.weighted | 172 |
| abstract_inverted_index.yielding | 147 |
| abstract_inverted_index.accuracy. | 64 |
| abstract_inverted_index.addresses | 67 |
| abstract_inverted_index.different | 137 |
| abstract_inverted_index.four-base | 170 |
| abstract_inverted_index.proposing | 71 |
| abstract_inverted_index.reduction | 61 |
| abstract_inverted_index.weighting | 76 |
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| abstract_inverted_index.challenges | 34 |
| abstract_inverted_index.confidence | 74 |
| abstract_inverted_index.deployment | 89 |
| abstract_inverted_index.distances, | 56 |
| abstract_inverted_index.engineers. | 25 |
| abstract_inverted_index.introduces | 83 |
| abstract_inverted_index.scenarios. | 98 |
| abstract_inverted_index.ultimately | 146 |
| abstract_inverted_index.(TOF)-based | 28 |
| abstract_inverted_index.Experiments | 151 |
| abstract_inverted_index.algorithms. | 175 |
| abstract_inverted_index.application | 14 |
| abstract_inverted_index.categorizes | 117 |
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| abstract_inverted_index.susceptibility | 38 |
| abstract_inverted_index.time-of-flight | 27 |
| abstract_inverted_index.decision-making | 143 |
| abstract_inverted_index.two-dimensional | 148 |
| abstract_inverted_index.self-optimization | 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.17091645 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |